Nitric Oxide-Releasing Porous Coating with Antibacterial Activity and Blood Compatibility.

Nitric oxide (NO)-releasing coating is promising to enhance the biocompatibility of medical devices. In this study, polyurethane (PU) and S-nitrosated keratin (KSNO) were dissolved with dimethyl sulfoxide (DMSO) and tetrahydrofuran (THF) to prepare a coating solution. This solution is facile to form a porous coating on various substrates based on solvent-evaporation-induced phase separation (SEIPS). The coating could continuously release NO up to 200 h in the presence of ascorbic acid (Asc). In addition, the coating could accelerate endothelialization by promoting the viability of human umbilical vein endothelial cells (HUVECs) while inhibiting the proliferation of human umbilical artery smooth muscle cells (HUASMCs). Furthermore, the coating had good antibacterial activity and blood compatibility. Taken together, this universal coating provides wider potential applications in the field of cardiovascular implants.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.

BACKGROUND: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. METHOD: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. RESULTS: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. CONCLUSION: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

Exceptional Non-Abelian Topology in Multiband Non-Hermitian Systems.

Defective spectral degeneracy, known as exceptional point (EP), lies at the heart of various intriguing phenomena in optics, acoustics, and other nonconservative systems. Despite extensive studies in the past two decades, the collective behaviors (e.g., annihilation, coalescence, braiding, etc.) involving multiple exceptional points or lines and their interplay have been rarely understood. Here we put forward a universal non-Abelian conservation rule governing these collective behaviors in generic multiband non-Hermitian systems and uncover several counterintuitive phenomena. We demonstrate that two EPs with opposite charges (even the pairwise created) do not necessarily annihilate, depending on how they approach each other. Furthermore, we unveil that the conservation rule imposes strict constraints on the permissible exceptional-line configurations. It excludes structures like Hopf link yet permits novel staggered rings composed of noncommutative exceptional lines. These intriguing phenomena are illustrated by concrete models which could be readily implemented in platforms like coupled acoustic cavities, optical waveguides, and ring resonators. Our findings lay the cornerstone for a comprehensive understanding of the exceptional non-Abelian topology and shed light on the versatile manipulations and applications based on exceptional degeneracies in nonconservative systems.

Association between serum calcium level and type 2 diabetes: An NHANES analysis and Mendelian randomization study.

AIMS: This study investigated the association between serum calcium levels and the prevalence of T2D using a cross-sectional study and Mendelian randomization analysis. METHODS: Cross-sectional data were obtained from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Serum calcium levels were divided into three groups (low, medium and high groups) according to the tertiles. Logistic regression was used to estimate the association between serum calcium levels and T2D prevalence. Instrumental variables for serum calcium levels were obtained from the UK Biobank and a two-sample MR analysis was performed to examine the causal relationship between genetically predicted serum calcium levels and the risk of T2D. RESULTS: A total of 39,645 participants were available for cross-sectional analysis. After adjusting for covariates, participants in the high serum calcium group had significantly higher odds of T2D (OR = 1.18, 95% CI = 1.07, 1.30, p = 0.001) than those in the moderate group. Restricted cubic spline plots showed a J-shaped curve relationship between serum calcium level and prevalence of T2D. Consistently, Mendelian randomization analysis showed that higher genetically predicted serum calcium levels were causally associated with a higher risk of T2D (OR = 1.16, 95% CI: 1.01, 1.33, p = 0.031). CONCLUSIONS: The results of this study suggest that higher serum calcium levels are causally associated with a higher risk of T2D. Further studies are needed to clarify whether intervening in high serum calcium could reduce the risk of T2D.

Knots and Non-Hermitian Bloch Bands.

Knots have a twisted history in quantum physics. They were abandoned as failed models of atoms. Only much later was the connection between knot invariants and Wilson loops in topological quantum field theory discovered. Here we show that knots tied by the eigenenergy strings provide a complete topological classification of one-dimensional non-Hermitian (NH) Hamiltonians with separable bands. A Z_{2} knot invariant, the global biorthogonal Berry phase Q as the sum of the Wilson loop eigenphases, is proved to be equal to the permutation parity of the NH bands. We show the transition between two phases characterized by distinct knots occur through exceptional points and come in two types. We further develop an algorithm to construct the corresponding tight-binding NH Hamiltonian for any desired knot, and propose a scheme to probe the knot structure via quantum quench. The theory and algorithm are demonstrated by model Hamiltonians that feature, for example, the Hopf link, the trefoil knot, the figure-8 knot, and the Whitehead link.

Topological Invariants for Quantum Quench Dynamics from Unitary Evolution.

Recent experiments began to explore the topological properties of quench dynamics, i.e., the time evolution following a sudden change in the Hamiltonian, via tomography of quantum gases in optical lattices. In contrast to the well-established theory for static band insulators or periodically driven systems, at present it is not clear whether, and how, topological invariants can be defined for a general quench of band insulators. Previous work solved a special case of this problem beautifully using Hopf mapping of two-band Hamiltonians in two dimensions. However, it only works for a topologically trivial initial state and is hard to generalize to multiband systems or other dimensions. Here we introduce the concept of loop unitary constructed from the unitary time-evolution operator and show its homotopy invariant fully characterizes the dynamical topology. For two-band systems in two dimensions, we prove that the invariant is precisely equal to the change in the Chern number across the quench, regardless of the initial state. We further show that the nontrivial dynamical topology manifests as hedgehog defects in the loop unitary and also as winding and linking of its eigenvectors along a curve where dynamical quantum phase transition occurs. This opens up a systematic route to classify and characterize quantum quench dynamics.

Dynamical Singularities of Floquet Higher-Order Topological Insulators.

We propose a versatile framework to dynamically generate Floquet higher-order topological insulators by multistep driving of topologically trivial Hamiltonians. Two analytically solvable examples are used to illustrate this procedure to yield Floquet quadrupole and octupole insulators with zero- and/or π-corner modes protected by mirror symmetries. Furthermore, we introduce dynamical topological invariants from the full unitary return map and show its phase bands contain Weyl singularities whose topological charges form dynamical multipole moments in the Brillouin zone. Combining them with the topological index of a Floquet Hamiltonian gives a pair of Z_{2} invariant ν_{0} and ν_{π} which fully characterize the higher-order topology and predict the appearance of zero- and π-corner modes. Our work establishes a systematic route to construct and characterize Floquet higher-order topological phases.

Majorana Doublets, Flat Bands, and Dirac Nodes in s-Wave Superfluids.

Topological superfluids protected by mirror and time-reversal symmetries are exotic states of matter possessing Majorana Kramers pairs (MKPs), yet their realizations have long been hindered by the requirement of unconventional pairing. We propose to realize such a topological superfluid by utilizing s-wave pairing and emergent mirror and time-reversal symmetries in two coupled 1D ultracold atomic Fermi gases with spin-orbit coupling. By stacking such systems into 2D, we discover topological and Dirac-nodal superfluids hosting distinct MKP flat bands. We show that the emergent symmetries make the MKPs and their flat bands stable against pairing fluctuations that otherwise annihilate Majorana pairs. Exploiting new experimental developments, our scheme provides a unique platform for exploring MKPs and their applications in quantum computation.

Superfluid-Quasicrystal in a Bose-Einstein Condensate.

A quasicrystal is a class of ordered structures defying conventional classification of solid crystals and may carry classically forbidden (e.g., fivefold) rotational symmetries. In view of long-sought supersolids, a natural question is whether a superfluid can spontaneously form quasicrystalline order that is not possessed by the underlying Hamiltonian, forming "superfluid-quasicrystals." Here we show that a superfluid-quasicrystal stripe state with the minimal fivefold rotational symmetry can be realized as the ground state of a Bose-Einstein condensate within a practical experimental scheme. There exists a rich phase diagram consisting of various superfluid-quasicrystal, supersolid, and plane-wave phases. Our scheme can be generalized for generating other higher-order (e.g., sevenfold) quasicrystal states, and provides a platform for investigating such new exotic quantum matter.

Topological Triply Degenerate Points Induced by Spin-Tensor-Momentum Couplings.

The recent discovery of triply degenerate points (TDPs) in topological materials has opened a new perspective toward the realization of novel quasiparticles without counterparts in quantum field theory. The emergence of such protected nodes is often attributed to spin-vector-momentum couplings. We show that the interplay between spin-tensor- and spin-vector-momentum couplings can induce three types of TDPs, classified by different monopole charges (C=±2, ±1, 0). A Zeeman field can lift them into Weyl points with distinct numbers and charges. Different TDPs of the same type are connected by intriguing Fermi arcs at surfaces, and transitions between different types are accompanied by level crossings along high-symmetry lines. We further propose an experimental scheme to realize such TDPs in cold-atom optical lattices. Our results provide a framework for studying spin-tensor-momentum coupling-induced TDPs and other exotic quasiparticles.

Slowing the expansion of impervious surfaces: the key to promoting high-quality and sustainable regional development.

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4-5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study.

Background: In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs. Methods: We utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship. Results: In the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89-7.88; PIVW < 0.00625), while a potential association exists between PM2.5-10 and vitiligo (OR = 7.42; 95% CI: 1.02-53.94; PIVW < 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17-2.1e+03; PIVW < 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW > 0.025). There was no pleiotropy in the results. Conclusion: Our results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.

Evaluating the risk of developing hyperuricemia in patients with type 2 diabetes mellitus using least absolute shrinkage and selection operator regression and machine learning algorithm.

Background: Hyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease. Methods: Patients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated. Results: A total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%. Conclusions: We developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus.

Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.

Polymorphism of follicle stimulating hormone beta (FSHß) subunit gene and its association with litter traits in giant panda.

The different SSCP patterns of the follicle stimulating hormone beta (FSHß) gene amplified by three pairs of primers were sequenced. Comparisons among the three nucleotide sequences of three genotypes indicated that three base substitutions (A213T, A91G, and A89C) were detected in FSHß gene, which A213T substitution led to one amino acids mutation (Lys > Met), and the other two substitutions were synonymous mutations. The AA, AB and BB genotypes patterns obtained by FSHß primer1 had evident relation with the litter traits, but the SSCP genotypes patterns obtained by FSHß primer2 and primer3 had no evident relation with the litter traits in giant panda. The giant panda with AA and AB genotype had the largest litter size and multiparity rate compared with the BB genotypes (P < 0.05). We speculated that the giant pandas with the A allele have better litter traits than those with the B allele.

Floquet non-Abelian topological insulator and multifold bulk-edge correspondence.

Topological phases characterized by non-Abelian charges are beyond the scope of the paradigmatic tenfold way and have gained increasing attention recently. Here we investigate topological insulators with multiple tangled gaps in Floquet settings and identify uncharted Floquet non-Abelian topological insulators without any static or Abelian analog. We demonstrate that the bulk-edge correspondence is multifold and follows the multiplication rule of the quaternion group Q8. The same quaternion charge corresponds to several distinct edge-state configurations that are fully determined by phase-band singularities of the time evolution. In the anomalous non-Abelian phase, edge states appear in all bandgaps despite trivial quaternion charge. Furthermore, we uncover an exotic swap effect-the emergence of interface modes with swapped driving, which is a signature of the non-Abelian dynamics and absent in Floquet Abelian systems. Our work, for the first time, presents Floquet topological insulators characterized by non-Abelian charges and opens up exciting possibilities for exploring the rich and uncharted territory of non-equilibrium topological phases.

Changes of microbiota level in urinary tract infections: A meta-analysis.

No consensus has been reached on the dysbiosis signs of microbiota in patients with urinary tract infections (UTIs). This meta-analysis aimed to verify the relationship between microbiota levels and UTIs. PubMed, Web of Science, and Embase databases were retrieved for related articles published from inception until October 20, 2021. The standardized mean difference (SMD) and its related 95% confidence intervals (CIs) of the microbiota diversity and abundance were pooled under a random-effects model. Twelve studies were included in this meta-analysis. The pooled analysis revealed that the microbiota diversity was lower in patients with UTIs than in healthy individuals (SMD = -0.655, 95% CI = -1.290, -0.021, I 2 = 81.0%, P = 0.043). The abundance of specific bacteria was higher in UTI subjects compared with healthy control individuals (SMD = 0.41, 95% CI = 0.07-0.74, P = 0.017), especially in North America patients with UTIs. Similar results were also found in studies with the total sample size being greater than 30. Importantly, Escherichia coli levels were increased in patients with UTI, whereas Lactobacillus levels decreased. E. coli and Lactobacilli have huge prospects as potential microbiota markers in the treatment of UTIs.

Effect of Conventional Nursing Combined with Bedtime Oculomotor Training on Sleep Quality and Body Immunity of Advanced Lung Cancer Patients.

Objective: The aim of this study is to explore the effect of conventional nursing combined with bedtime oculomotor training on sleep quality and body immune of advanced lung cancer patients. Methods: By means of a retrospective study, 120 advanced lung cancer patients admitted to our hospital from January 2019 to January 2020 were selected as the research subject and divided into the intervention group (PSQI (Pittsburgh Sleep Quality Index) score≥10 points, n = 60) and the control group (PSQI score<10 points, n = 60). Conventional nursing was performed to the control group, and an eye movement exercise before sleep was added additionally in the intervention group, 30 min each time, once a day, and 5 times a week for 3 months, so as to compare their sleep quality, body immunity indexes, negative emotion scores, adverse reaction rate (ARR), quality of life, and satisfaction with nursing. Results: After nursing, the intervention group obtained a significantly lower PSQI score (5.54 ± 1.23 VS 7.98 ± 1.65, P < 0.05), better body immunity indexes (P < 0.001), lower negative emotion scores (P < 0.05), lower ARR (P < 0.05), better quality of life (P < 0.001), and higher satisfaction with nursing (P < 0.05) than the control group. Conclusion: Combining conventional nursing with the eye movement exercise before sleep can alleviate negative emotions, improve the sleep quality, promote body immunity, and reduce the ARR, which is more satisfying to patients and should be applied and promoted in practice.

Geomorphic processes of a dammed palaeo-lake in the middle Yarlung Tsangpo River, Tibet.

The failure of a natural dam is an extreme geological event. Palaeo-lake sediments were discovered in the broad Xigazê valley and Dazhuka-Yueju gorge in the middle reach of the Yarlung Tsangpo River in Tibet. However, the sedimentary processes, dam failure, and peak flood of the Xigazê dammed palaeo-lake are poorly understood. Hence, we conducted a field survey of eight lacustrine sedimentary terraces in the area. We divided the sedimentary processes of the palaeo-lake into five stages and deposit types: pre-palaeo-lake sediments (fluvial or aeolian deposits); early stage sediments of the palaeo-lake (coarse sand); main stage palaeo-lake sediments (clayey silt and sand), sediments following the discharge of the palaeo-lake (sand and gravel-cobbles); and cover deposits (aeolian sediments and colluvium). Additionally, the water level along the palaeo-lake was almost constant (3811 m a.s.l.). The dam was likely located at the eastern end of the Dazhuka-Yueju gorge. Based on the water level, dam location and 30-m ASTER GDEM2 data, the capacity of the palaeo-lake was estimated as 22.55 km3. To separate the water volume and sediment volume, the sediment surface elevation along the palaeo-lake was simulated based on the elevations of the six lacustrine sedimentary terraces. The volume of the sediment was ~11.56 km3, which was calculated from the dam location, sediment surface elevation, and the ASTER GDEM2 data. Finally, subtraction of the sediment volume from the capacity of the palaeo-lake gave a backwater volume of 10.99 km3. The peak flood possibly exceeded 3.4 × 105 m3/s as a moraine dam joined the discharge during the dam failure. However, the dammed event probably had a limited effect on the landforms at downstream because of the presence of another dammed palaeo-lake in the broad Zetang valley; moreover, the bedrock upstream of the dam was protected from erosion.